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On the efficient and fast response for sensor deployment in sparse wireless sensor networks
Available online at www.sciencedirect.com
Computer Communications 30 (2007) 3892–3903 www.elsevier.com/locate/comcom
On the efficient and fast response for sensor deployment in sparse wireless sensor networks q Ben-Jye Chang b
a,*
, Jia-Bin Peng
b
a Department of Computer Science & Information Engineering, Chaoyang University of Technology, Taichung, Taiwan, ROC Graduate Institute of Networking and Communication Engineering, Chaoyang University of Technology, Taichung, Taiwan, ROC
Received 11 September 2006; received in revised form 27 September 2007; accepted 2 October 2007 Available online 11 October 2007
Abstract Wireless sensor networks have recently become new techniques and popular research issues. A wireless sensor network consists of a large number of sensor nodes that have the capabilities of sensing, computing and wireless transmission. Wireless sensor networks (namely WSNs) assist people in working under dangerous environments, provide long-term target observations and track on moving objects. Consequently, WSNs decrease risk and increase efficiency. Although WSNs have been studied extensively, several problems should be addressed, such as sensor-deployment policy, data aggregation/fusion issue, and data transmission issue. An efficient sensor-deployment approach could decrease cost, minimize transmission delay and reduce time complexity. Most studies have proposed the probability-based sensor-deployment policies to monitor an overall area. However, not the entire network is interested to be sensed/monitored. Monitoring of an entire area brings several disadvantages: (1) high cost of placing large number of sensors, (2) long delay of data transmission, (3) slow response and (4) unnecessary data aggregation. Furthermore, previous works were lack of considering the difference between the sensing and the transmission radii, and then yield inaccurate analysis. This work thus proposes an efficient sensor placement approach (namely ESP) for a sparse interested area with considering of obstructers that block the data transmission and sensing signal. Additionally, the issue of different radii of sensing and transmission is analyzed in detail. Numerical results demonstrate that the proposed ESP approach requires the least number of sensor nodes under various network sizes and different number of obstacles. Simulation results indicate that the number of sensor nodes decreases when the sensing or transmission radius increases. The running time of ESP, O(K2), is also analyzed, which is better than that of the probability-based approaches, O(N2), where K is the number of interested grids and N is the number of grids. Ó 2007 Elsevier B.V. All rights reserved. Keywords: Wireless sensor networks; Placement policy; Obstacle; Re-deploy; Area coverage
1. Introduction This section first introduces the wireless sensor network (WSN), and then describes the issues in WSNs. Next, related works are detailed. Finally, the motivations of this work are depicted. Several research techniques have been presented extensively to monitor specified areas and to q This research was supported in part by the National Science Council of Taiwan, ROC, under Contract NSC-94-2213-E-324-015. * Corresponding author. Tel.: +886 4 23323000x4526; fax: +886 4 23742375. E-mail addresses: [email protected] (B.-J. Chang), s9230612@ mail.cyut.edu.tw (J.-B. Peng).
0140-3664/$ - see front matter Ó 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.comcom.2007.10.004
gather sensed information through WSNs. A WSN consists of a large number of sensor nodes which have the capabilities of target sensing, data computing and packet transmission through wireless links. Akyildiz et al. [1] have briefly introduced the functions and applications of WSNs. For instance, many applications about the information reporting in WSN have been studied, including monitoring environments, gathering information, and tracking targets. In WSN, a sensor node receives a query message from a supervisor node, i.e., the border node. It thus senses and gathers the required information through sensing components, and then aggregates the information by computing units. Finally, the processed information is sent to the bor-
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der node through intermediate sensor nodes. The network administrator could manage/monitor the interested area from any hosts on the Internet. Hwang et al. [2] briefly introduced the structure of WSNs. In [3], Hsieh proposed a WSN-based monitor application of transportation traffic, in which the administrator directly monitors the areas happened traffic jam, and then gathers the information from sensor nodes. As comparing the WSN-based monitoring system with the traditional one, the WSN-based approach achieves the advantages of efficient and economical. Several issues in WSN have proposed in [4–12], such as the sensordeployment policy, routing issue, data collection and data aggregation, and objects tracking. However, the sensor-deployment policy is the essential important issue that should be solved for achieving efficient application in WSN. Deploying too many sensor nodes for an interested area is costly and increases data transmission delay. Conversely, if the sensor nodes are deployed not enough, the uncovered areas cannot be sensed and become dead areas. The administrator could not query/gather the information to/from these dead areas. Consequently, a good sensor placement policy could minimize the number of sensor nodes, simplify the data aggregation and reduce the length of the data forwarding path. The sensor-deployment policy can be classified into two aspects [5]. The first aspect of classification is the coverage type, which categories as the coverages of area, point and barrier. In the area coverage, the placement should place sensors till all interesting areas can be sensed [17,20]. In the point coverage, the objective is to cover a set of points [16,17]. In the barrier coverage, it is to minimize the undetected probability blocked by barriers [15,18,19]. The second aspect of classification is the placement method, which is categorized as the random and deterministic placements. In the random placement, the sensor location is not known a priori [13–16,18–20]. Conversely, in the deterministic placement, sensors can be placed exactly where they are needed [17]. Moreover, the proposed placement algorithms are either centralized [15–18] or distributed [19– 21]. Heo and Varshney proposed three distributed deployments [21]. Since they were lack of considering re-deployment when some deployed sensors are fail, these algorithms are only suitable for static sensor deployment. In previous studies, most approaches adopted the probability-based random placement with considering the area coverage partitioned by a large number of sensing-units, called grids, as indicated in Fig. 3. These algorithms assume that all grids in the network are interested grids that should be sensed by sensor nodes. That is unsubstantial. Additionally, they need to determine a sensed threshold used for deciding whether a target grid is sensed or not. Since the threshold is a tradeoff between the sensed probability of the network and the number of placed sensors, the threshold determination requires a systematical analysis mechanism. As a result, the probability-based
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algorithms are not suitable for real networks with obstacles. In [13], Benyuan and Towsley proposed two placement methods for WSN. First is a grid-based method that computes the sensing probability of each grid that is sensed by all placed neighbor sensor nodes. If the sensing probability of a grid does not exceed a predefined threshold, the grid will be placed a sensor node within it. Otherwise, the grid will not be placed a sensor node. The method is completed when all grids are sensed. Second is the random placement method, in which the sensing probability of each grid is computed after a sensor node is randomly placed, until the sensing probability of each grid exceeds the defined threshold. Since these two methods assume all grids should be covered and did not deal with obstacles in WSN, the algorithms are not suitable for real environments that have various types of obstacles, e.g., buildings, walls, trees, rocks and mountains. In [14], Dhillon and Chakrabarty proposed two placement methods for WSN. The first method, namely MAX_AVG_COV, attempts to maximize the average coverage of the grid points. The second method, namely MAX_MIN_COV, attempts to maximize the coverage of the grid point that is covered least effectively. MAX_AVG_COV assumes that the sensor nodes are placed into a grid in turn, and then calculates the sum of miss probabilities of other grids. The unplaced grid with the least sum of miss probability will be placed sensor nodes. MAX_AVG_COV is completed when the miss probabilities of all grids are less than a defined threshold T. In contrast to MAX_AVG_COV, MAX_MIN_COV calculates the miss probabilities of grids after randomly places the first sensor node. The grid with the highest miss probability will be selected to place a sensor node. The sensor placement is completed when the nonsensed probabilities of all grids are less than the defined threshold. As shown in [6], MAX_MIN_COV requires less number of sensor nodes than that of MAX_AVG_COV. Although both of the MAX_AVG_COV and MAX_MIN_COV algorithms considered of obstacles, the probability-based mechanism cannot work well in real environments. Moreover, how to determine the optimal threshold is another issue in [6] that should be addressed. Since most studies adopted grid probability-based for entire network area and they were lack of considering the actual geography environment (e.g., buildings and obstacles) that may block or reduce the signal strength of wireless transmission, these approaches result in large number of placed sensor nodes and yield inaccurate results. Meanwhile, another disadvantage of the probability-based method is that the sensing probability of grids is not always 100%. That means the placed sensor nodes cannot guarantee to sense some interested grids 100%. Furthermore, they did not differentiate the sensing radius from the wireless transmission radius, which impact on the placement results. Therefore, this work proposes an efficient sensor placement (ESP) approach for deploying sensors in a sparse
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WSN. The main contributions of this paper include: (1) to minimize the number of sensor nodes based on area coverage type with considering obstacles and different radii of sensing and wireless transmission and (2) to re-deploy new sensors dynamically when the interested grids are changed or the original deployed sensors are failed. The remainder of the paper organized as follows. Section 2 describes the environment of wireless sensor networks and defines the network model. Section 3 describes the efficient sensor placement approach that we proposed. The results of simulation will be presented and discussed in Section 4. Section 5 draws conclusions and specifies areas of future works. 2. Network model In this section, we first introduce the characteristics of WSN and then define the network model of WSN. We model a WSN as a graph, G = (V, E), which consists of a set of mobile nodes, V, and a set of wireless links, E. Each wireless sensor node, s, consists of components of sensing, computing and wireless transmission. Assume that each sensor node has the same transmission radius of Rt and the same sensing radius of Rs. Any two sensor nodes can communicate with each other if the distance between them is less than the transmission range, Rt. The notations used in this paper are defined as follows: L the length of a wireless sensor network W the width of a wireless sensor network N the number of grids in the sensor network K the number of interested grids Q the number of obstacles b the border node O the obstacle Rt the transmission radius of each sensor node Rs the sensing radius of each sensor node d the half length of the diagonal line of a grid pgffiffiffi 2d g the length of a grid Unfinished{} the set of unfinished interested grids Success{} the set of sensed interested grids (i.e., the interested grid that has been placed a sensor or can be sensed by other sensors) Block{} the set of interested grids located at dead areas, i.e., they are surrounded by obstacles Fig. 1 is an example of a WSN, in which the type of area coverage is considered. Each interested grid has at least one sensor node within it or the grid can be sensed by neighbor sensors. At least one border node in the WSN acts as a gateway to communicate between the administrator node and the WSN. The border node is randomly selected from the grids located on the boundaries of the network. In this paper, we consider obstacles for real environment. Any directly transmission between two sensor nodes will be blocked if at least one obstacle exists between these two sensor nodes. A grid is non-sen-
Fig. 1. An example of a wireless sensor network.
sible if at least one obstacle exists between the grid and a placed sensor node. Moreover, the placement of sensor nodes in this work is based on building a minimum distance tree, so the sensors’ positions are not known a priori. In this paper, we evaluate the proposed placement algorithm for WSNs by evaluating the number of placed sensor nodes under various sizes of WSN, different number of obstacles, different number of interested grids, different sensing radii and different transmission radii. Requiring less number of sensor nodes yields the advantages of lower cost, shorter delay of data aggregated, and faster response. In addition, for evaluating the coverage of placed sensor nodes, we compare the rate of sensed but fail to transmit, denoted by SBFT, which is defined by SBFT ¼
NFS ; NPS
where NFS is the number of interested grids which can be sensed but cannot communicate with other sensors and NPS is the total number of placed sensors. Lower SBFT means better coverage. 3. Efficient sensor placement approach This section describes the proposed ESP approach in detail. ESP includes three motivations. First, the placement approaches in several studies seldom simultaneously considered both the sensing and wireless transmission radii, but these two important parameters should be addressed in sensor networks. Both the sensing and wireless transmission radii are thus considered in the proposed ESP approach, which are extended from [22]. Second, most approaches adopted the probability-based centralized method to deploy sensors to cover the entire WSN, which unsatisfied real environments and required large number of sensors. For covering all interested grids with less number of sensors, ESP is proposed based on non-deterministic area coverage with the minimum distance tree algorithm to deploy sensors in a sparse WSN. The centralized based ESP achieves some advantages, including minimizing the number of sensor nodes,
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reducing the data transmission delay and supporting adaptive re-deploying. Third, ESP adopts an adaptive re-deployment mechanism to place some new sensor nodes when the placed sensors are failure or some interested grids are added. ESP thus increases the network reliability. Furthermore, the time complexity of ESP is analyzed. The analytic results demonstrate that the running time of ESP outperforms that of the probabilitybased approach.
For describing ESP in a clarity style, the procedure of ESP is partitioned into five steps, as shown in Fig. 2, including Step 1: determining the shortest path from the interested grids in Unfinished{} to the determined minimum distance tree and then detecting obstacles on the shortest path, Step 2: placing extra sensor nodes on the shortest path,
Initial
1. Bypass dead areas 2. Determine the sensor position and the number of sensor nodes in a grid
Step1 1. Find the shortest path 2. Any obstacle exists on the shortest path?
No
Yes
Block{} ← the indexes of
No
these two grids;
Step 2 Place extra sensor nodes successfully?
Step 4: Check the distance between these two grids
Yes
Yes
d > Rt ?
Place intermediate node(s)
Yes
No
d > Rs ?
Place a sensor node
No
Success{} ← the index of the interested grid;
No
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Step 5 All interested grids are sensed? Yes END
Fig. 2. The ESP procedures.
Step 3 Check the interested grid that can be sensed by other placed sensor nodes
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Step 3: checking whether the interested grid can be sensed by other placed sensor nodes or not, Step 4: checking whether the distance between two grids requires placing extra intermediate nodes or not, and Step 5: checking whether all interested grids are sensed or not. In a grid-based sensor network, the entire network is partitioned into a large array of grids, as indicated in Fig. 1. The coordinate, (x, y), for a grid or an obstacle can be determined by a relative-position method or by a globe position system (GPS). In real sensor networks, obstacles could block data communication and sensing signal. This paper considers some randomly placed obstacles within a wireless sensor network. The interested grids surrounded by obstacles are marked as dead areas. Since these sensor nodes placed in a dead area could not receive/transmit data from/to outside sensor nodes, it is unnecessary to place sensor nodes in dead areas. Three types of dead area are considered in this work, as demonstrated in Fig. 3. By using the sequential traversal algorithm [23], ESP sets the link between any two neighboring obstacles as a partial-loop link. We assume that there are Q obstacles in a WSN. Since each obstacle has at most eight neighbors, ESP requires O(8 Æ Q), i.e., O(Q), running time to check whether neighbors are obstacles or not. A new partial-loop link is added to a loop-candidate tree structure. If the new added obstacle in the tree has two parents, the loop-candidate tree structure forms a close loop and a dead area is obtained. The running time of checking whether the new added obstacle in the tree has two parents or not is O(1). Consequently, ESP requires O(Q) running time for determining dead areas. The sensing or transmission radius of a sensor node may differ from the grid size. The placed location of a sensor node in a grid (e.g., the central or border of a grid) is considered herein to reduce the number of placed sensor nodes. We define the ratio of Rs to dg as n, i.e., Rs n¼ ; ð1Þ dg where n has two cases. First, in the case of n P 1, the sensing scope of a sensor node is greater than the grid area. If
(a) An interested grid is in a border of a WSN Interested grids
(b) obstacles surrounds an interested grid
(c) obstacles surrounds an interested grid
Obstacle grids
Fig. 3. Three types of dead area.
n%2 = 1, the sensor node is placed at the grid center. Conversely, if n%2 = 0, the sensor node is placed at the grid border. Second, in the case of n < 1, the sensing scope is less than the grid area. Thus, several sensor nodes are required to cover an entire grid. The number of placed sensor nodes within a grid denoted by Gn is determined as & 2’ dg Gn ¼ : ð2Þ R2s After determining the dead areas, the sensor position and the number of placed sensor nodes in a grid, ESP performs the following five steps. 3.1. Step 1: The step of determining the shortest path from the interested grids in Unfinished{} to the determined minimum distance tree, and then detecting obstacles on the shortest path Step 1 performs two procedures: (1) finding an interested grid in the Unfinished{} set, which has the minimum distance to the minimum distance tree, and (2) detecting whether the minimum distance path exists any obstacle or not. The grid-based sensor network is aware of the coordinates of the border and each grid. The border node is the root of the minimum distance tree built for data transmissions among the border and sensor nodes. First, ESP computes the straight distance between each tree node and each interested grid in Unfinished{}. The straight distance between a tree node t and an interested grid n is expressed by qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Dstraight ð3Þ ¼ ðxt xn Þ2 þ ðy t y n Þ2 ; t;n where (x, y)t and (x, y)n are the coordinates of the tree node t and the interested grid n, respectively. After that, ESP uses the selection algorithm [23] to determine the interested grid in Unfinished{}, which has the minimum distance to the tree, as the candidate tree node. The comparison algorithm requires O(K) running time, where K is the number of interested grids. Second, ESP detects whether the minimum distance path exists any obstacle or not. Although the radio signal can be transmitted through multiple paths in wireless transmission, we consider the case of direct transmission along a single path for its simple, and thus ignore the multiple paths problem. Assume that the coordinates of two-end grids A and B on the minimum distance path are (XA, YA) and (XB, YB), respectively, where one is the sensor node on the tree and the other is in Unfinished{}. Any obstacles within the rectangle formed from grids A and B will be checked because these obstacles may block the transmission between the grids A and B. Assume that there is at least one obstacle O within the rectangle, as shown in Fig. 4. The check procedure first determines the distance d between the obstacle O and line L that crosses grids A and B, where (XO, YO) is the coordinate of obstacle O
B.-J. Chang, J.-B. Peng / Computer Communications 30 (2007) 3892–3903 sensor node A, (XA,YA)
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A
5
L : Y = m( X − X A ) + YA
3 1
d
2 . .
Obstacle node O, (XO,YO)
Line M
.
4 Interested grid B, (XB,YB)
Line L
Fig. 4. The step of detecting obstacle on the shortest path.
B and dg is the half length of the diagonal line of a grid. Thus, we have j mX O þ Y O þ cj pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; m2 þ 1 YB YA ; m¼ XB XA
d¼
ð4Þ ð5Þ
and c ¼ mX A þ Y A :
ð6Þ
If d 6 dg, it means that at least one obstacle exists between grids A and B. ESP then enters into Step 2 to place another sensor node within the rectangle to relay the data transmission, as shown in Fig. 4. Otherwise, in the case of d > dg, it means that no obstacle exists between grids A and B. ESP enters into Step 3 to check whether the interested grid can be sensed by the placed sensor nodes or not. 3.2. Step 2: The step of placing extra sensor nodes on the shortest path If an obstacle exists between grids A and B, ESP will place some extra sensor nodes within the rectangle formed by these two grids to relay the data transmission. The relay sensor node I should be placed on a line M, which is perpendicular to line L. The placement order of the replay sensor node is indicated in Fig. 5, which starts from the closest grid to the intersection of lines M and L, e.g., the grid 1. If the placement is failure, i.e., it still exists other obstacles between grid 1 and grid A or B, the closest grid on the other side on line M should be checked, i.e., the grid 2, and so on. Otherwise, ESP enters into Step 4 to check the distance between the successfully placed relay node and grid A/B. If the procedure of placing extra sensor nodes is failure, the indexes of grids A and B will be put into the block set, Block{}. ESP selects two grids at the ends of the second shortest path and then enters into Step 1 again. The
Obstacle
Possible position of the intermediate sensor node
Fig. 5. The placing order of the intermediate sensor node.
algorithm only considers the grids on line M within the rectangle, which is enough and efficient. There are two primary reasons. First, if we place relay nodes into the grids that are outside the rectangle, too many relay nodes will be placed for the transmission between grids A and B. Second, if any relay nodes on line M within the rectangle are blocked, the transmission between grids A and B can not be completed. The determination of placing relay nodes is efficient and thus reduces the computation time. 3.3. Step 3: The step of checking whether the interested grid can be sensed by other placed sensor nodes or not Since no obstacle exists between two interested grids or between an interested grid and a sensor node, ESP checks whether the distance between them, d, is less than the sensing radius, Rs, or not. If d > Rs, ESP places sensor nodes to the interested grids, and then enters into Step 4 to check whether the distance between these two sensor nodes is less than the transmission radius Rt or not. On the other hand, if d 6 Rs, ESP places a sensor node to one of these two interested grids in the case of two interested grids, or ESP does not place any sensor node to the interested grid in the case of an interested grid and a sensor node. This work considers the difference between the sensing radius and transmission radius. In the case of Rs > Rt, a sensor node with a large sensing radius could sense more interested grids, and then requires less number of sensor nodes. In the case of Rs < Rt, the number of intermediate nodes between the interested grid and placed sensor nodes could be reduced because fewer sensor nodes are required for intermediating transmissions.
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3.4. Step 4: The step of checking whether the distance between two grids requires placing extra intermediate nodes or not Since no obstacle exists between two interested grids or between an interested grid and a sensor node, ESP checks whether the distance d between them is less than the transmission radius Rt or not. If d 6 Rt, it is unnecessary to place an intermediate sensor node between these two interested grids. If d > Rt, the interested grid cannot transmit directly to the other. ESP has to place some other sensor nodes as intermediate nodes to relay transmissions, as shown in Fig. 6. Consequently, the intermediate nodes between these two interested grids are placed every the distance of the transmission radius. After placing some intermediate sensor nodes, ESP checks whether the distance d between the interested grid and the closest placed intermediate node is less than the sensing radius Rs or not. If d 6 Rs, ESP does not place any sensor node to the interested grid. Because of the intermediate sensor node could sense the interested grid. If d > Rs, ESP places a sensor node to the interested grid. The index of the interested grid is thus added to the successful set, Success{}. For instance, as shown in Fig. 6, three intermediate sensor nodes are needed to relay transmissions, but it does not place a sensor node into the right interested grid for above reason. Finally, ESP enters into Step 5 to check whether all interested grids are considered or not. 3.5. Step 5: The step of checking whether all interested areas are sensed or not The final step is to determine whether all interested grids can be sensed by all placed sensor nodes and the sensed information could be transmitted to the border node or not, i.e., check whether Unfinished{} = NULL or not. If not, it enters into Step 1. If yes, all the interested grids are sensed and the ESP algorithm is completed. Moreover, ESP dynamically re-deploys sensors when the interested grids are changed or the original deployed sensors fail.
After performing above five procedures for deploying sensors, ESP covers all the interested grids, except those in dead areas. The main contributions of ESP is to minimize the total number of sensors, and to reduce the deploy cost and data transmission delay. Since sensor nodes may fail or out of power, it degrades the reliability of ESP. For increasing reliability, ESP re-deploys dynamically new sensors when the interested grids are changed or the original deployed sensors are failure. Finally, the algorithm of the proposed ESP approach is shown in Fig. 7 and the running time complexity of the ESP algorithm is analyzed as follows. Now, we analyze the time complexity of the ESP algorithm. Assume that the network has N grids and K interested grids to be placed sensor nodes. Since in this work we consider sparse interested grids in the network, K is much less than N, i.e., 0 < K > N. Initially, the Unfinished{} set stores K interested grids, and the Success{} or Block{} sets are set to null. ESP needs O(N) time to determine obstacles that are excluded to place any sensor node. If the number of interested grids in Unfinished{} is greater than zero, ESP will find the interested grid in Unfinished{} that has the shortest distance to the determined shortest path tree or to the border node. ESP is completed until all the K interested grids in Unfinished{} PKare considered. ESP requires the running time of O K1 m¼0 m ðK mÞ to complete the ESP algorithm, where m, 0 6 m 6 K, is the number of interested grids that have been considered, i.e., each considered grid is either in Success{} or in Block{}. That is K 1 X m ðK mÞ K m¼0
1 ½1 ðK 1Þ þ 2 ðK 2Þ þ K þ ðK 1Þ ½K ðK 1Þ; 1 ¼ ½K þ 2K þ K 2 þ ðK 1ÞK ½12 þ 22 þ þ ðK 1Þ ; 1 ½K þ ðK 1ÞK ðK 1ÞKð2K 1Þ ðK 1Þ ; ¼ K 2 6 3 1 K K ¼ ; K 6 ¼
¼
Fig. 6. The placing of intermediate sensor nodes as Rt < d.
K2 1 : 6
Therefore, the worst case of time complexity of ESP is O(K2). We compare the running time complexity of ESP with those of the probability-based approaches of MAX_AVG_COV and MAX_MIN_COV. Assume that the sensor network has N grids. The probability-based approaches need the running time of O(N) for each grid to determine the sum of non-sensed probabilities from others and require O(N) time to determine the grid that has
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Fig. 7. The ESP algorithm.
7.0E+06
approaches. Fig. 8 demonstrates the results of running time of compared approaches under different network sizes. In Fig. 8, ESP significantly outperforms the probability-based approach in running time under different percentages of interested grids in various network sizes.
Probability-based
6.0E+06
5% NDS_ESP 10% NDS_ESP 15% NDS_ESP
Running time
5.0E+06
20% NDS_ESP 25% NDS_ESP
4.0E+06
30% NDS_ESP
4. Numerical results
35%NDS_ESP
3.0E+06
40% NDS_ESP
2.0E+06
1.0E+06 0.0E+00 100
400
900
1600
2500
Network size
Fig. 8. The running time of ESP and the probability-based approaches under various network sizes.
minimum sum of non-sensed probability. Since each of these two approaches has N grids, they require O(N Æ N) = O(N2) in running time, which are worse than that of ESP, O(K2). If the number of interested grids in ESP increases up to N, the running time of ESP becomes O(N2) that is the same as those of the probability-based
This section evaluates the number of placed sensor nodes of the proposed ESP approach and several compared approaches under different sizes of WSN, number of interested grids and number of obstacles. The first compared placement algorithm proposed in [13] is denoted as Random. The second and third approaches are denoted as MAX_AVG_COV and MAX_MIN_COV [14]. In this paper, we investigate the impact of different sensing and wireless transmission radii. Thus, the number of sensor nodes under various sensing and wireless transmission radii are evaluated, in which the transmission radius, Rt, and the sensing radius, Rs, are set from two to six times of the grid size. Additionally, three thresholds used in the compared approaches of MAX_AVG_COV and MAX_MIN_COV include: (1) the sensing probability of interested grids, namely Ti, (2) the sensing probability of non-interested grids, namely Tg, and (3) the decay parameter, a, of the
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Table 1 Simulation parameters
350
Parameter
Value
NDS: the number of grids
100, 400, 900, 1600, 2500 20, 40, 60, 80, 100 0, 10, 20, 30, 40, 50 2, 3, 4, 5, 6 2, 3, 4, 5, 6 0.1 0.2
300
MAX_MIN_COV Random
number of sensors
NIS: the number of interested grids NBS: the number of obstacles Rt: the transmission radius Rs: the sensing radius Ti: the sensing probability of interested grid Tg: the sensing probability of non-interested grid a: the decay parameter of the sensing probability
ESP MAX_AVG_COV
250
200
150
100
0.5 50
sensing probability, ead, where d is the distance between a sensor and an interested grid. The simulation parameters are shown in Table 1. The 95% confidence intervals of the simulation results in the following figures originate from 25 independent runs. For each run, the simulated time is 1100 units of mean connection holding time. The initial 100 time units are considered the transient period and performance samples are thus discarded. A graphic user interface-based simulation tool is developed, which is coded by Java. The number of placed sensor nodes of different approaches under various network sizes of NIS = 8 and NBS = 8 is demonstrated in Fig. 9. The number of placed sensor nodes of Random, MAX_AVG_COV and MAX_MIN_COV increase significantly as the network size increases, but that of ESP increases gently as the network size increasing. In Fig. 10, the number of placed sensor nodes of different approaches under various NBS of NIS = 20 and NDS = 400 are evaluated, where Rt is set to 2, Ti = 0.1, Tg = 0.2, and a = 0.5. Fig. 10 indicates that the number of sensor nodes of MAX_AVG_COV and MAX_MIN_ COV increase as the number of obstacles increases; especially, that of Random significantly increases as the number
0 0
10
20
30
40
50
NBS
Fig. 10. The number of sensor nodes under various NBS (fixed NIS and NDS).
of obstacles increases. The primary reason is that MAX_AVG_COV and MAX_MIN_COV are the probability-based approaches, which needs extra sensor nodes for increasing the total sensed probability when the sensed probability is lower than the defined threshold. However, ESP is not based on the sensed probability, and then not affected by the number of obstacles. That is a good feature of an efficient sensor-deployment approach. Consequently, ESP needs fewer intermediate nodes as the number of obstacle grids increasing. The number of sensor nodes of different approaches under various NIS of NDS = 400 and 20% obstacles is demonstrated in Fig. 11. In Fig. 11, the numbers of sensor nodes of Random and ESP increase obviously, but gentle in MAX_AVG_COV and MAX_MIN_COV as NIS increasing. ESP yields the least number of sensor nodes. The reason is ESP determines the optimal grid has the shortest distance to the determined minimum distance tree for deploying intermediate nodes. Consequently, ESP yields
1200
ESP
200
800
MAX_AVG_COV
number of sensors
number of sensors
240
ESP MAX_AVG_COV MAX_MIN_COV Random
1000
600
400
MAX_MIN_COV 160
Random
120
200
80 0 100
400
900
1600
2500
NDS
Fig. 9. The number of sensor nodes under various NDS (fixed NIS and NBS).
40
5%
10%
15%
20%
25%
30%
NIS (%)
Fig. 11. The number of sensor nodes under various NIS.
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120
200
NIS=20
160
NIS=60
number of sensors
number of sensors
NIS=40
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40 ESP MAX_MIN_AVG MAX_AGV_COV Random
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Fig. 12. The number of sensor nodes various under extra added NIS.
Fig. 14. The number of sensor nodes of ESP under different sensing radii (Rt = 2).
the least number of sensor nodes while having a large number of obstacles in WSN. Since some placed sensors may fail or some interested grids may be added, ESP adopts adaptive re-deployment mechanism to re-deploy some new sensors based on deployed sensor nodes. Consequently, ESP only requires few new sensors to cover uncovered interested grids. Fig. 12 evaluates the number of placed sensors under extra added 20% obstacles. The numbers of new placed sensor nodes of all approaches increase as the extra added NIS increases. ESP yields the least result when the number of extra added NIS is less than 50. On the other hand, the other approaches seem not to be affected by the extra added NIS. The reason is the probability-based approaches deploy too many sensor nodes in the WSN, even deploy sensor nodes to uninterested grids or dead areas. As a result, the probability-based approaches do not require more extra sensor nodes when the number of extra added NIS increases.
The number of sensor nodes of all evaluated approaches under various obstacle sizes is shown in Fig. 13, in which the obstacle size is selected from 1, random of (1, 4) and random of (1, 4, 9). As Fig. 13 shown, the number of sensor nodes of Random increases significantly as the obstacle size increases. Additionally, those of MAX_AVG_COV and MAX_MIN_COV increase gently as the obstacle size increases. ESP does not affected by the obstacle size obviously. The number of sensor nodes of ESP under different sensing radii is shown in Fig. 14, where NDS = 400, NBS = 20 and Rt = 2. The numbers of sensor nodes of different NIS decrease as the sensing radius increasing. Since a sensor node with a larger sensing radius could cover more number of interested grids, ESP requires less number of placed sensors. The number of sensor nodes of ESP under different transmission radii is indicated in Fig. 15, where NDS = 400, NBS = 20 and Rs = 2. The numbers of sensor nodes of different NIS decrease as the transmission radius 120
number of sensors
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NIS=20 NIS=40 NIS=60 NIS=80 NIS=100
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Fig. 13. The number of sensor nodes under various obstacle sizes.
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Fig. 15. The number of sensor nodes of ESP under different transmission radii (Rs = 2).
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Fig. 16. Sensed but fail to transmit (SBFT) of different approaches under various NDS (Rs = 2, Rt = 1, NBS = 20).
increasing, because a longer transmission range requires less number of intermediate sensor nodes. For evaluating the coverage of placed sensor nodes, we compare the rate of sensed but fail to transmit (i.e., SBFT) of all approaches under various NDS, as indicated in Fig. 16. Lower SBFT means better coverage. In Fig. 16, ESP yields 0 SBFT, i.e., ESP achieves 100% coverage for all interested grids. The Random approach yields competitive SBFT to ESP. MAX_MIN_COV yields the highest
SBFT, which has the worst coverage. The SBFT of MAX_ AVG_COV is higher than that of Random. Additionally, the SBFTs of MAX_MIN_COV and MAX_AVG_COV increase as NDS increasing. The reason of the coverage results in Fig. 16 is that ESP considers the sensing and transmission radii simultaneously while placing sensor nodes, but other approaches do not. Finally, the placed results of all approaches with NDS = 400, NBS = 20 and NIS = 15 under random obstacle sizes of 1, 4 or 9 are demonstrated in Fig. 17a–d. ESP yields the least number of sensor nodes for covering all interested grids, but the others require more number of sensor nodes. In summary, ESP deploys the least number of sensor nodes as compared to other approaches under various situations, including different sizes of WSN, different number of interested grids, different number of obstacles grids, and different obstacle sizes. Additionally, ESP obviously reduces the number of sensor nodes while the sensing radius or the transmission radius increasing. 5. Conclusions and future works Not all the areas of a real environment are interested for sensing or monitoring as the network size increasing. The sensor node placement for covering entire filed causes several disadvantages, including costly, inefficient data forwarding and long transmission delay. Additionally, the
Fig. 17. The placement results of four approaches under the same environment, where the green circle is a border, the blue circles are sensor nodes, the pink squares are interested grids, and the black grids are obstacles. (a) The placement result of ESP. (b) The placement result of MMAX_MIN_COV. (c) The placement result of MAX_AVG_COV. (d) The placement result of Random. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
B.-J. Chang, J.-B. Peng / Computer Communications 30 (2007) 3892–3903
probability-based placement approaches are not suitable for such a WSN with sparse interested grids. An efficient sensor placement approach is thus proposed to deploy sensor nodes for covering interested grids in a sparse WSN. Obstacles are considered in ESP to meet real environments, where obstacles may block the data transmission between two sensor nodes. It thus requires some extra intermediate sensor nodes to relay data transmission. Moreover, different sensing and transmission radii are considered in ESP. Numerical results demonstrated that ESP outperforms other approaches in the number of placed sensor nodes under various situations, including different sizes of WSN, number of interested grids, number of obstacles and SBFT. ESP thus reduces sensor-deployment cost and data transmission delay. For increasing reliability, ESP adopts a dynamic re-deployment mechanism to place new sensors or to add some extra interested grids when some placed sensors fail. Finally, the worst case of time complexity of ESP, O(K2), is analyzed, which is better than that of the probability-based approaches, O(N2), where K is the number of interested grids and N is the number of grids. Many aspects of dynamic routing of sensed information in the wireless sensor networks require further study. For instance, we are currently investigating an efficient routing for distributed the query message in WSN with supporting local repair mechanism to increase the transmission reliability and the packet delivery rate. References [1] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey, Computer Networks 38 (4) (2002) 393– 422. [2] K.-I. Hwang, J. In, N. Park, D.-S. Eom, A design and implementation of wireless sensor gateway for efficient querying and managing through world wide web, IEEE Transactions on Consumer Electronics 49 (4) (2003) 1090–1097. [3] T.T. Hsieh, Using sensor networks for highway and traffic applications, IEEE Potentials 23 (2) (2004) 13–16. [4] D. Ganesan, A. Cerpa, W. Ye, Y. Yu, J. Zhao, D. Estrin, Networking issues in wireless sensor networks, Journal of Parallel and Distributed Computing 64 (7) (2004) 799–814. [5] M. Cardei, J. Wu, Coverage in wireless sensor networks, Handbook of Sensor Networks, CRC Press, 2004. [6] J. Carle, D. Simplot, Energy efficient area monitoring by sensor networks, IEEE Computer 37 (2) (2004) 40–46. [7] J. Zhang, H. Shi, Energy-efficient routing for 2D grid wireless sensor networks, in: Proceedings ITRE2003, August 2003, pp. 311–315. [8] J.-H. Chang, L. Tassiulas, Maximum lifetime routing in wireless sensor networks, IEEE/ACM Transactions on Networking 12 (4) (2004) 609–619. [9] S. Lindsey, C. Raghavendra, K.M. Sivalingam, Data gathering algorithms in sensor networks using energy metrics, IEEE Transactions on Parallel and Distributed Systems 13 (9) (2002) 924–935. [10] W. Yuan, S.V. Krishnamurthy, S.K. Tripathi, Synchronization of multiple levels of data fusion in wireless sensor networks, in: IEEE Globecom’03, vol. 1, December 2003, pp. 221–225. [11] D. Stark, J. Davis, Friendly Object Tracking and Foreign Object Detection and Localization with an SDAC Wireless Sensor Network, in: FTDCS 2004, May 2004, pp. 30–36.
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[12] W.-P. Chen, J.C. Hou, S. Lui, Dynamic clustering for acoustic target tracking in wireless sensor networks, in: IEEE International Conference on Network Protocols, November 2003, pp. 284–294. [13] L. Benyuan, D. Towsley, On the coverage and detectability of largescale wireless sensor network, in: WiOpt’03: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, March 2003. [14] S.S. Dhillon, K. Chakrabarty, Sensor placement for effective coverage and surveillance in distributed sensor networks, in: WCNC 2003, vol. 3, March 2003, pp. 1609–1614. [15] S. Adlakha, M. Srivastava, Critical density thresholds for coverage in wireless sensor networks, IEEE Wireless Communications and Networking 3 (2003) 16–20. [16] M. Cardei, D.-Z. Du, Improving wireless sensor network lifetime through power aware organization, Wireless Networks 11 (3) (2005) 333–340. [17] K. Kar, S. Banerjee, Node placement for connected coverage in sensor networks, in: Proceedings of WiOpt 2003: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, March 2003. [18] S. Meguerdichian, F. Koushanfar, M. Potkonjak, M. Srivastava, Coverage problems in wireless ad-hoc sensor networks, in: IEEE Infocom 2001, vol. 3, April 2001, pp. 1380–1387. [19] X.-Y. Li, P.-J. Wan, O. Frieder, Coverage in wireless ad-hoc sensor networks, IEEE Transactions on Computers 52 (6) (2003) 753–763. [20] X. Wang, G. Xing, Y. Zhang, C. Lu, R. Pless, C.D. Gill, Integrated coverage and connectivity configuration in wireless sensor networks, in: First ACM Conference on Embedded Networked Sensor Systems (SenSys’03), November 2003, pp. 28–39. [21] N. Heo, P.K. Varshney, Energy-efficient deployment of intelligent mobile sensor networks, IEEE Transactions on Systems, Man and Cybernetics 1 (1) (2005) 78–92. [22] B.-J. Chang, J.-B. Peng, Y.-H. Liang, Minimizing transmission delay and deployment cost for sensors placement in sparse wireless sensor networks, in: IEEE WCNC2007, March 2007, pp. 1954–1958. [23] T.H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein, Introduction to Algorithms, The MIT Press, 2001.
Ben-Jye Chang received his M.S. degree in computer engineering from University of Massachusetts, Lowell, in 1991 and the Ph.D. degree in computer science and information engineering from National Chung-Cheng University, Taiwan, in 2001. He joined the Department of Computer Science and Information Engineering faculty at Chaoyang University of Technology, Taiwan, in 2002, where he is currently an Associate Professor. His research interests include seamless roaming/vertical handoff in heterogeneous wireless networks, mobile computing in wireless sensor networks, QoS-based wireless networks, resource management for mobile communications, and TCP congestion control in heterogeneous wireless networks.
Jia-Bin Peng received his M.S. degree in networking and communication engineering from Chaoyang University of Technology, Taichung, Taiwan, in 2005. His current research interests include sensor deployment in wireless sensor networks, wireless networking and mobile computing, and all IP-based mobile communications.
Computer Communications 30 (2007) 3892–3903 www.elsevier.com/locate/comcom
On the efficient and fast response for sensor deployment in sparse wireless sensor networks q Ben-Jye Chang b
a,*
, Jia-Bin Peng
b
a Department of Computer Science & Information Engineering, Chaoyang University of Technology, Taichung, Taiwan, ROC Graduate Institute of Networking and Communication Engineering, Chaoyang University of Technology, Taichung, Taiwan, ROC
Received 11 September 2006; received in revised form 27 September 2007; accepted 2 October 2007 Available online 11 October 2007
Abstract Wireless sensor networks have recently become new techniques and popular research issues. A wireless sensor network consists of a large number of sensor nodes that have the capabilities of sensing, computing and wireless transmission. Wireless sensor networks (namely WSNs) assist people in working under dangerous environments, provide long-term target observations and track on moving objects. Consequently, WSNs decrease risk and increase efficiency. Although WSNs have been studied extensively, several problems should be addressed, such as sensor-deployment policy, data aggregation/fusion issue, and data transmission issue. An efficient sensor-deployment approach could decrease cost, minimize transmission delay and reduce time complexity. Most studies have proposed the probability-based sensor-deployment policies to monitor an overall area. However, not the entire network is interested to be sensed/monitored. Monitoring of an entire area brings several disadvantages: (1) high cost of placing large number of sensors, (2) long delay of data transmission, (3) slow response and (4) unnecessary data aggregation. Furthermore, previous works were lack of considering the difference between the sensing and the transmission radii, and then yield inaccurate analysis. This work thus proposes an efficient sensor placement approach (namely ESP) for a sparse interested area with considering of obstructers that block the data transmission and sensing signal. Additionally, the issue of different radii of sensing and transmission is analyzed in detail. Numerical results demonstrate that the proposed ESP approach requires the least number of sensor nodes under various network sizes and different number of obstacles. Simulation results indicate that the number of sensor nodes decreases when the sensing or transmission radius increases. The running time of ESP, O(K2), is also analyzed, which is better than that of the probability-based approaches, O(N2), where K is the number of interested grids and N is the number of grids. Ó 2007 Elsevier B.V. All rights reserved. Keywords: Wireless sensor networks; Placement policy; Obstacle; Re-deploy; Area coverage
1. Introduction This section first introduces the wireless sensor network (WSN), and then describes the issues in WSNs. Next, related works are detailed. Finally, the motivations of this work are depicted. Several research techniques have been presented extensively to monitor specified areas and to q This research was supported in part by the National Science Council of Taiwan, ROC, under Contract NSC-94-2213-E-324-015. * Corresponding author. Tel.: +886 4 23323000x4526; fax: +886 4 23742375. E-mail addresses: [email protected] (B.-J. Chang), s9230612@ mail.cyut.edu.tw (J.-B. Peng).
0140-3664/$ - see front matter Ó 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.comcom.2007.10.004
gather sensed information through WSNs. A WSN consists of a large number of sensor nodes which have the capabilities of target sensing, data computing and packet transmission through wireless links. Akyildiz et al. [1] have briefly introduced the functions and applications of WSNs. For instance, many applications about the information reporting in WSN have been studied, including monitoring environments, gathering information, and tracking targets. In WSN, a sensor node receives a query message from a supervisor node, i.e., the border node. It thus senses and gathers the required information through sensing components, and then aggregates the information by computing units. Finally, the processed information is sent to the bor-
B.-J. Chang, J.-B. Peng / Computer Communications 30 (2007) 3892–3903
der node through intermediate sensor nodes. The network administrator could manage/monitor the interested area from any hosts on the Internet. Hwang et al. [2] briefly introduced the structure of WSNs. In [3], Hsieh proposed a WSN-based monitor application of transportation traffic, in which the administrator directly monitors the areas happened traffic jam, and then gathers the information from sensor nodes. As comparing the WSN-based monitoring system with the traditional one, the WSN-based approach achieves the advantages of efficient and economical. Several issues in WSN have proposed in [4–12], such as the sensordeployment policy, routing issue, data collection and data aggregation, and objects tracking. However, the sensor-deployment policy is the essential important issue that should be solved for achieving efficient application in WSN. Deploying too many sensor nodes for an interested area is costly and increases data transmission delay. Conversely, if the sensor nodes are deployed not enough, the uncovered areas cannot be sensed and become dead areas. The administrator could not query/gather the information to/from these dead areas. Consequently, a good sensor placement policy could minimize the number of sensor nodes, simplify the data aggregation and reduce the length of the data forwarding path. The sensor-deployment policy can be classified into two aspects [5]. The first aspect of classification is the coverage type, which categories as the coverages of area, point and barrier. In the area coverage, the placement should place sensors till all interesting areas can be sensed [17,20]. In the point coverage, the objective is to cover a set of points [16,17]. In the barrier coverage, it is to minimize the undetected probability blocked by barriers [15,18,19]. The second aspect of classification is the placement method, which is categorized as the random and deterministic placements. In the random placement, the sensor location is not known a priori [13–16,18–20]. Conversely, in the deterministic placement, sensors can be placed exactly where they are needed [17]. Moreover, the proposed placement algorithms are either centralized [15–18] or distributed [19– 21]. Heo and Varshney proposed three distributed deployments [21]. Since they were lack of considering re-deployment when some deployed sensors are fail, these algorithms are only suitable for static sensor deployment. In previous studies, most approaches adopted the probability-based random placement with considering the area coverage partitioned by a large number of sensing-units, called grids, as indicated in Fig. 3. These algorithms assume that all grids in the network are interested grids that should be sensed by sensor nodes. That is unsubstantial. Additionally, they need to determine a sensed threshold used for deciding whether a target grid is sensed or not. Since the threshold is a tradeoff between the sensed probability of the network and the number of placed sensors, the threshold determination requires a systematical analysis mechanism. As a result, the probability-based
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algorithms are not suitable for real networks with obstacles. In [13], Benyuan and Towsley proposed two placement methods for WSN. First is a grid-based method that computes the sensing probability of each grid that is sensed by all placed neighbor sensor nodes. If the sensing probability of a grid does not exceed a predefined threshold, the grid will be placed a sensor node within it. Otherwise, the grid will not be placed a sensor node. The method is completed when all grids are sensed. Second is the random placement method, in which the sensing probability of each grid is computed after a sensor node is randomly placed, until the sensing probability of each grid exceeds the defined threshold. Since these two methods assume all grids should be covered and did not deal with obstacles in WSN, the algorithms are not suitable for real environments that have various types of obstacles, e.g., buildings, walls, trees, rocks and mountains. In [14], Dhillon and Chakrabarty proposed two placement methods for WSN. The first method, namely MAX_AVG_COV, attempts to maximize the average coverage of the grid points. The second method, namely MAX_MIN_COV, attempts to maximize the coverage of the grid point that is covered least effectively. MAX_AVG_COV assumes that the sensor nodes are placed into a grid in turn, and then calculates the sum of miss probabilities of other grids. The unplaced grid with the least sum of miss probability will be placed sensor nodes. MAX_AVG_COV is completed when the miss probabilities of all grids are less than a defined threshold T. In contrast to MAX_AVG_COV, MAX_MIN_COV calculates the miss probabilities of grids after randomly places the first sensor node. The grid with the highest miss probability will be selected to place a sensor node. The sensor placement is completed when the nonsensed probabilities of all grids are less than the defined threshold. As shown in [6], MAX_MIN_COV requires less number of sensor nodes than that of MAX_AVG_COV. Although both of the MAX_AVG_COV and MAX_MIN_COV algorithms considered of obstacles, the probability-based mechanism cannot work well in real environments. Moreover, how to determine the optimal threshold is another issue in [6] that should be addressed. Since most studies adopted grid probability-based for entire network area and they were lack of considering the actual geography environment (e.g., buildings and obstacles) that may block or reduce the signal strength of wireless transmission, these approaches result in large number of placed sensor nodes and yield inaccurate results. Meanwhile, another disadvantage of the probability-based method is that the sensing probability of grids is not always 100%. That means the placed sensor nodes cannot guarantee to sense some interested grids 100%. Furthermore, they did not differentiate the sensing radius from the wireless transmission radius, which impact on the placement results. Therefore, this work proposes an efficient sensor placement (ESP) approach for deploying sensors in a sparse
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WSN. The main contributions of this paper include: (1) to minimize the number of sensor nodes based on area coverage type with considering obstacles and different radii of sensing and wireless transmission and (2) to re-deploy new sensors dynamically when the interested grids are changed or the original deployed sensors are failed. The remainder of the paper organized as follows. Section 2 describes the environment of wireless sensor networks and defines the network model. Section 3 describes the efficient sensor placement approach that we proposed. The results of simulation will be presented and discussed in Section 4. Section 5 draws conclusions and specifies areas of future works. 2. Network model In this section, we first introduce the characteristics of WSN and then define the network model of WSN. We model a WSN as a graph, G = (V, E), which consists of a set of mobile nodes, V, and a set of wireless links, E. Each wireless sensor node, s, consists of components of sensing, computing and wireless transmission. Assume that each sensor node has the same transmission radius of Rt and the same sensing radius of Rs. Any two sensor nodes can communicate with each other if the distance between them is less than the transmission range, Rt. The notations used in this paper are defined as follows: L the length of a wireless sensor network W the width of a wireless sensor network N the number of grids in the sensor network K the number of interested grids Q the number of obstacles b the border node O the obstacle Rt the transmission radius of each sensor node Rs the sensing radius of each sensor node d the half length of the diagonal line of a grid pgffiffiffi 2d g the length of a grid Unfinished{} the set of unfinished interested grids Success{} the set of sensed interested grids (i.e., the interested grid that has been placed a sensor or can be sensed by other sensors) Block{} the set of interested grids located at dead areas, i.e., they are surrounded by obstacles Fig. 1 is an example of a WSN, in which the type of area coverage is considered. Each interested grid has at least one sensor node within it or the grid can be sensed by neighbor sensors. At least one border node in the WSN acts as a gateway to communicate between the administrator node and the WSN. The border node is randomly selected from the grids located on the boundaries of the network. In this paper, we consider obstacles for real environment. Any directly transmission between two sensor nodes will be blocked if at least one obstacle exists between these two sensor nodes. A grid is non-sen-
Fig. 1. An example of a wireless sensor network.
sible if at least one obstacle exists between the grid and a placed sensor node. Moreover, the placement of sensor nodes in this work is based on building a minimum distance tree, so the sensors’ positions are not known a priori. In this paper, we evaluate the proposed placement algorithm for WSNs by evaluating the number of placed sensor nodes under various sizes of WSN, different number of obstacles, different number of interested grids, different sensing radii and different transmission radii. Requiring less number of sensor nodes yields the advantages of lower cost, shorter delay of data aggregated, and faster response. In addition, for evaluating the coverage of placed sensor nodes, we compare the rate of sensed but fail to transmit, denoted by SBFT, which is defined by SBFT ¼
NFS ; NPS
where NFS is the number of interested grids which can be sensed but cannot communicate with other sensors and NPS is the total number of placed sensors. Lower SBFT means better coverage. 3. Efficient sensor placement approach This section describes the proposed ESP approach in detail. ESP includes three motivations. First, the placement approaches in several studies seldom simultaneously considered both the sensing and wireless transmission radii, but these two important parameters should be addressed in sensor networks. Both the sensing and wireless transmission radii are thus considered in the proposed ESP approach, which are extended from [22]. Second, most approaches adopted the probability-based centralized method to deploy sensors to cover the entire WSN, which unsatisfied real environments and required large number of sensors. For covering all interested grids with less number of sensors, ESP is proposed based on non-deterministic area coverage with the minimum distance tree algorithm to deploy sensors in a sparse WSN. The centralized based ESP achieves some advantages, including minimizing the number of sensor nodes,
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reducing the data transmission delay and supporting adaptive re-deploying. Third, ESP adopts an adaptive re-deployment mechanism to place some new sensor nodes when the placed sensors are failure or some interested grids are added. ESP thus increases the network reliability. Furthermore, the time complexity of ESP is analyzed. The analytic results demonstrate that the running time of ESP outperforms that of the probabilitybased approach.
For describing ESP in a clarity style, the procedure of ESP is partitioned into five steps, as shown in Fig. 2, including Step 1: determining the shortest path from the interested grids in Unfinished{} to the determined minimum distance tree and then detecting obstacles on the shortest path, Step 2: placing extra sensor nodes on the shortest path,
Initial
1. Bypass dead areas 2. Determine the sensor position and the number of sensor nodes in a grid
Step1 1. Find the shortest path 2. Any obstacle exists on the shortest path?
No
Yes
Block{} ← the indexes of
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these two grids;
Step 2 Place extra sensor nodes successfully?
Step 4: Check the distance between these two grids
Yes
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d > Rt ?
Place intermediate node(s)
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d > Rs ?
Place a sensor node
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Success{} ← the index of the interested grid;
No
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Step 5 All interested grids are sensed? Yes END
Fig. 2. The ESP procedures.
Step 3 Check the interested grid that can be sensed by other placed sensor nodes
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Step 3: checking whether the interested grid can be sensed by other placed sensor nodes or not, Step 4: checking whether the distance between two grids requires placing extra intermediate nodes or not, and Step 5: checking whether all interested grids are sensed or not. In a grid-based sensor network, the entire network is partitioned into a large array of grids, as indicated in Fig. 1. The coordinate, (x, y), for a grid or an obstacle can be determined by a relative-position method or by a globe position system (GPS). In real sensor networks, obstacles could block data communication and sensing signal. This paper considers some randomly placed obstacles within a wireless sensor network. The interested grids surrounded by obstacles are marked as dead areas. Since these sensor nodes placed in a dead area could not receive/transmit data from/to outside sensor nodes, it is unnecessary to place sensor nodes in dead areas. Three types of dead area are considered in this work, as demonstrated in Fig. 3. By using the sequential traversal algorithm [23], ESP sets the link between any two neighboring obstacles as a partial-loop link. We assume that there are Q obstacles in a WSN. Since each obstacle has at most eight neighbors, ESP requires O(8 Æ Q), i.e., O(Q), running time to check whether neighbors are obstacles or not. A new partial-loop link is added to a loop-candidate tree structure. If the new added obstacle in the tree has two parents, the loop-candidate tree structure forms a close loop and a dead area is obtained. The running time of checking whether the new added obstacle in the tree has two parents or not is O(1). Consequently, ESP requires O(Q) running time for determining dead areas. The sensing or transmission radius of a sensor node may differ from the grid size. The placed location of a sensor node in a grid (e.g., the central or border of a grid) is considered herein to reduce the number of placed sensor nodes. We define the ratio of Rs to dg as n, i.e., Rs n¼ ; ð1Þ dg where n has two cases. First, in the case of n P 1, the sensing scope of a sensor node is greater than the grid area. If
(a) An interested grid is in a border of a WSN Interested grids
(b) obstacles surrounds an interested grid
(c) obstacles surrounds an interested grid
Obstacle grids
Fig. 3. Three types of dead area.
n%2 = 1, the sensor node is placed at the grid center. Conversely, if n%2 = 0, the sensor node is placed at the grid border. Second, in the case of n < 1, the sensing scope is less than the grid area. Thus, several sensor nodes are required to cover an entire grid. The number of placed sensor nodes within a grid denoted by Gn is determined as & 2’ dg Gn ¼ : ð2Þ R2s After determining the dead areas, the sensor position and the number of placed sensor nodes in a grid, ESP performs the following five steps. 3.1. Step 1: The step of determining the shortest path from the interested grids in Unfinished{} to the determined minimum distance tree, and then detecting obstacles on the shortest path Step 1 performs two procedures: (1) finding an interested grid in the Unfinished{} set, which has the minimum distance to the minimum distance tree, and (2) detecting whether the minimum distance path exists any obstacle or not. The grid-based sensor network is aware of the coordinates of the border and each grid. The border node is the root of the minimum distance tree built for data transmissions among the border and sensor nodes. First, ESP computes the straight distance between each tree node and each interested grid in Unfinished{}. The straight distance between a tree node t and an interested grid n is expressed by qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Dstraight ð3Þ ¼ ðxt xn Þ2 þ ðy t y n Þ2 ; t;n where (x, y)t and (x, y)n are the coordinates of the tree node t and the interested grid n, respectively. After that, ESP uses the selection algorithm [23] to determine the interested grid in Unfinished{}, which has the minimum distance to the tree, as the candidate tree node. The comparison algorithm requires O(K) running time, where K is the number of interested grids. Second, ESP detects whether the minimum distance path exists any obstacle or not. Although the radio signal can be transmitted through multiple paths in wireless transmission, we consider the case of direct transmission along a single path for its simple, and thus ignore the multiple paths problem. Assume that the coordinates of two-end grids A and B on the minimum distance path are (XA, YA) and (XB, YB), respectively, where one is the sensor node on the tree and the other is in Unfinished{}. Any obstacles within the rectangle formed from grids A and B will be checked because these obstacles may block the transmission between the grids A and B. Assume that there is at least one obstacle O within the rectangle, as shown in Fig. 4. The check procedure first determines the distance d between the obstacle O and line L that crosses grids A and B, where (XO, YO) is the coordinate of obstacle O
B.-J. Chang, J.-B. Peng / Computer Communications 30 (2007) 3892–3903 sensor node A, (XA,YA)
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A
5
L : Y = m( X − X A ) + YA
3 1
d
2 . .
Obstacle node O, (XO,YO)
Line M
.
4 Interested grid B, (XB,YB)
Line L
Fig. 4. The step of detecting obstacle on the shortest path.
B and dg is the half length of the diagonal line of a grid. Thus, we have j mX O þ Y O þ cj pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ; m2 þ 1 YB YA ; m¼ XB XA
d¼
ð4Þ ð5Þ
and c ¼ mX A þ Y A :
ð6Þ
If d 6 dg, it means that at least one obstacle exists between grids A and B. ESP then enters into Step 2 to place another sensor node within the rectangle to relay the data transmission, as shown in Fig. 4. Otherwise, in the case of d > dg, it means that no obstacle exists between grids A and B. ESP enters into Step 3 to check whether the interested grid can be sensed by the placed sensor nodes or not. 3.2. Step 2: The step of placing extra sensor nodes on the shortest path If an obstacle exists between grids A and B, ESP will place some extra sensor nodes within the rectangle formed by these two grids to relay the data transmission. The relay sensor node I should be placed on a line M, which is perpendicular to line L. The placement order of the replay sensor node is indicated in Fig. 5, which starts from the closest grid to the intersection of lines M and L, e.g., the grid 1. If the placement is failure, i.e., it still exists other obstacles between grid 1 and grid A or B, the closest grid on the other side on line M should be checked, i.e., the grid 2, and so on. Otherwise, ESP enters into Step 4 to check the distance between the successfully placed relay node and grid A/B. If the procedure of placing extra sensor nodes is failure, the indexes of grids A and B will be put into the block set, Block{}. ESP selects two grids at the ends of the second shortest path and then enters into Step 1 again. The
Obstacle
Possible position of the intermediate sensor node
Fig. 5. The placing order of the intermediate sensor node.
algorithm only considers the grids on line M within the rectangle, which is enough and efficient. There are two primary reasons. First, if we place relay nodes into the grids that are outside the rectangle, too many relay nodes will be placed for the transmission between grids A and B. Second, if any relay nodes on line M within the rectangle are blocked, the transmission between grids A and B can not be completed. The determination of placing relay nodes is efficient and thus reduces the computation time. 3.3. Step 3: The step of checking whether the interested grid can be sensed by other placed sensor nodes or not Since no obstacle exists between two interested grids or between an interested grid and a sensor node, ESP checks whether the distance between them, d, is less than the sensing radius, Rs, or not. If d > Rs, ESP places sensor nodes to the interested grids, and then enters into Step 4 to check whether the distance between these two sensor nodes is less than the transmission radius Rt or not. On the other hand, if d 6 Rs, ESP places a sensor node to one of these two interested grids in the case of two interested grids, or ESP does not place any sensor node to the interested grid in the case of an interested grid and a sensor node. This work considers the difference between the sensing radius and transmission radius. In the case of Rs > Rt, a sensor node with a large sensing radius could sense more interested grids, and then requires less number of sensor nodes. In the case of Rs < Rt, the number of intermediate nodes between the interested grid and placed sensor nodes could be reduced because fewer sensor nodes are required for intermediating transmissions.
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3.4. Step 4: The step of checking whether the distance between two grids requires placing extra intermediate nodes or not Since no obstacle exists between two interested grids or between an interested grid and a sensor node, ESP checks whether the distance d between them is less than the transmission radius Rt or not. If d 6 Rt, it is unnecessary to place an intermediate sensor node between these two interested grids. If d > Rt, the interested grid cannot transmit directly to the other. ESP has to place some other sensor nodes as intermediate nodes to relay transmissions, as shown in Fig. 6. Consequently, the intermediate nodes between these two interested grids are placed every the distance of the transmission radius. After placing some intermediate sensor nodes, ESP checks whether the distance d between the interested grid and the closest placed intermediate node is less than the sensing radius Rs or not. If d 6 Rs, ESP does not place any sensor node to the interested grid. Because of the intermediate sensor node could sense the interested grid. If d > Rs, ESP places a sensor node to the interested grid. The index of the interested grid is thus added to the successful set, Success{}. For instance, as shown in Fig. 6, three intermediate sensor nodes are needed to relay transmissions, but it does not place a sensor node into the right interested grid for above reason. Finally, ESP enters into Step 5 to check whether all interested grids are considered or not. 3.5. Step 5: The step of checking whether all interested areas are sensed or not The final step is to determine whether all interested grids can be sensed by all placed sensor nodes and the sensed information could be transmitted to the border node or not, i.e., check whether Unfinished{} = NULL or not. If not, it enters into Step 1. If yes, all the interested grids are sensed and the ESP algorithm is completed. Moreover, ESP dynamically re-deploys sensors when the interested grids are changed or the original deployed sensors fail.
After performing above five procedures for deploying sensors, ESP covers all the interested grids, except those in dead areas. The main contributions of ESP is to minimize the total number of sensors, and to reduce the deploy cost and data transmission delay. Since sensor nodes may fail or out of power, it degrades the reliability of ESP. For increasing reliability, ESP re-deploys dynamically new sensors when the interested grids are changed or the original deployed sensors are failure. Finally, the algorithm of the proposed ESP approach is shown in Fig. 7 and the running time complexity of the ESP algorithm is analyzed as follows. Now, we analyze the time complexity of the ESP algorithm. Assume that the network has N grids and K interested grids to be placed sensor nodes. Since in this work we consider sparse interested grids in the network, K is much less than N, i.e., 0 < K > N. Initially, the Unfinished{} set stores K interested grids, and the Success{} or Block{} sets are set to null. ESP needs O(N) time to determine obstacles that are excluded to place any sensor node. If the number of interested grids in Unfinished{} is greater than zero, ESP will find the interested grid in Unfinished{} that has the shortest distance to the determined shortest path tree or to the border node. ESP is completed until all the K interested grids in Unfinished{} PKare considered. ESP requires the running time of O K1 m¼0 m ðK mÞ to complete the ESP algorithm, where m, 0 6 m 6 K, is the number of interested grids that have been considered, i.e., each considered grid is either in Success{} or in Block{}. That is K 1 X m ðK mÞ K m¼0
1 ½1 ðK 1Þ þ 2 ðK 2Þ þ K þ ðK 1Þ ½K ðK 1Þ; 1 ¼ ½K þ 2K þ K 2 þ ðK 1ÞK ½12 þ 22 þ þ ðK 1Þ ; 1 ½K þ ðK 1ÞK ðK 1ÞKð2K 1Þ ðK 1Þ ; ¼ K 2 6 3 1 K K ¼ ; K 6 ¼
¼
Fig. 6. The placing of intermediate sensor nodes as Rt < d.
K2 1 : 6
Therefore, the worst case of time complexity of ESP is O(K2). We compare the running time complexity of ESP with those of the probability-based approaches of MAX_AVG_COV and MAX_MIN_COV. Assume that the sensor network has N grids. The probability-based approaches need the running time of O(N) for each grid to determine the sum of non-sensed probabilities from others and require O(N) time to determine the grid that has
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Fig. 7. The ESP algorithm.
7.0E+06
approaches. Fig. 8 demonstrates the results of running time of compared approaches under different network sizes. In Fig. 8, ESP significantly outperforms the probability-based approach in running time under different percentages of interested grids in various network sizes.
Probability-based
6.0E+06
5% NDS_ESP 10% NDS_ESP 15% NDS_ESP
Running time
5.0E+06
20% NDS_ESP 25% NDS_ESP
4.0E+06
30% NDS_ESP
4. Numerical results
35%NDS_ESP
3.0E+06
40% NDS_ESP
2.0E+06
1.0E+06 0.0E+00 100
400
900
1600
2500
Network size
Fig. 8. The running time of ESP and the probability-based approaches under various network sizes.
minimum sum of non-sensed probability. Since each of these two approaches has N grids, they require O(N Æ N) = O(N2) in running time, which are worse than that of ESP, O(K2). If the number of interested grids in ESP increases up to N, the running time of ESP becomes O(N2) that is the same as those of the probability-based
This section evaluates the number of placed sensor nodes of the proposed ESP approach and several compared approaches under different sizes of WSN, number of interested grids and number of obstacles. The first compared placement algorithm proposed in [13] is denoted as Random. The second and third approaches are denoted as MAX_AVG_COV and MAX_MIN_COV [14]. In this paper, we investigate the impact of different sensing and wireless transmission radii. Thus, the number of sensor nodes under various sensing and wireless transmission radii are evaluated, in which the transmission radius, Rt, and the sensing radius, Rs, are set from two to six times of the grid size. Additionally, three thresholds used in the compared approaches of MAX_AVG_COV and MAX_MIN_COV include: (1) the sensing probability of interested grids, namely Ti, (2) the sensing probability of non-interested grids, namely Tg, and (3) the decay parameter, a, of the
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Table 1 Simulation parameters
350
Parameter
Value
NDS: the number of grids
100, 400, 900, 1600, 2500 20, 40, 60, 80, 100 0, 10, 20, 30, 40, 50 2, 3, 4, 5, 6 2, 3, 4, 5, 6 0.1 0.2
300
MAX_MIN_COV Random
number of sensors
NIS: the number of interested grids NBS: the number of obstacles Rt: the transmission radius Rs: the sensing radius Ti: the sensing probability of interested grid Tg: the sensing probability of non-interested grid a: the decay parameter of the sensing probability
ESP MAX_AVG_COV
250
200
150
100
0.5 50
sensing probability, ead, where d is the distance between a sensor and an interested grid. The simulation parameters are shown in Table 1. The 95% confidence intervals of the simulation results in the following figures originate from 25 independent runs. For each run, the simulated time is 1100 units of mean connection holding time. The initial 100 time units are considered the transient period and performance samples are thus discarded. A graphic user interface-based simulation tool is developed, which is coded by Java. The number of placed sensor nodes of different approaches under various network sizes of NIS = 8 and NBS = 8 is demonstrated in Fig. 9. The number of placed sensor nodes of Random, MAX_AVG_COV and MAX_MIN_COV increase significantly as the network size increases, but that of ESP increases gently as the network size increasing. In Fig. 10, the number of placed sensor nodes of different approaches under various NBS of NIS = 20 and NDS = 400 are evaluated, where Rt is set to 2, Ti = 0.1, Tg = 0.2, and a = 0.5. Fig. 10 indicates that the number of sensor nodes of MAX_AVG_COV and MAX_MIN_ COV increase as the number of obstacles increases; especially, that of Random significantly increases as the number
0 0
10
20
30
40
50
NBS
Fig. 10. The number of sensor nodes under various NBS (fixed NIS and NDS).
of obstacles increases. The primary reason is that MAX_AVG_COV and MAX_MIN_COV are the probability-based approaches, which needs extra sensor nodes for increasing the total sensed probability when the sensed probability is lower than the defined threshold. However, ESP is not based on the sensed probability, and then not affected by the number of obstacles. That is a good feature of an efficient sensor-deployment approach. Consequently, ESP needs fewer intermediate nodes as the number of obstacle grids increasing. The number of sensor nodes of different approaches under various NIS of NDS = 400 and 20% obstacles is demonstrated in Fig. 11. In Fig. 11, the numbers of sensor nodes of Random and ESP increase obviously, but gentle in MAX_AVG_COV and MAX_MIN_COV as NIS increasing. ESP yields the least number of sensor nodes. The reason is ESP determines the optimal grid has the shortest distance to the determined minimum distance tree for deploying intermediate nodes. Consequently, ESP yields
1200
ESP
200
800
MAX_AVG_COV
number of sensors
number of sensors
240
ESP MAX_AVG_COV MAX_MIN_COV Random
1000
600
400
MAX_MIN_COV 160
Random
120
200
80 0 100
400
900
1600
2500
NDS
Fig. 9. The number of sensor nodes under various NDS (fixed NIS and NBS).
40
5%
10%
15%
20%
25%
30%
NIS (%)
Fig. 11. The number of sensor nodes under various NIS.
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120
200
NIS=20
160
NIS=60
number of sensors
number of sensors
NIS=40
100
120
80
NIS=80
80
NIS=100
60
40 ESP MAX_MIN_AVG MAX_AGV_COV Random
40
20
0
0 10
20
30
40
50
2
3
4
5
6
Sensin gradius (Rs)
Extra Added NIS
Fig. 12. The number of sensor nodes various under extra added NIS.
Fig. 14. The number of sensor nodes of ESP under different sensing radii (Rt = 2).
the least number of sensor nodes while having a large number of obstacles in WSN. Since some placed sensors may fail or some interested grids may be added, ESP adopts adaptive re-deployment mechanism to re-deploy some new sensors based on deployed sensor nodes. Consequently, ESP only requires few new sensors to cover uncovered interested grids. Fig. 12 evaluates the number of placed sensors under extra added 20% obstacles. The numbers of new placed sensor nodes of all approaches increase as the extra added NIS increases. ESP yields the least result when the number of extra added NIS is less than 50. On the other hand, the other approaches seem not to be affected by the extra added NIS. The reason is the probability-based approaches deploy too many sensor nodes in the WSN, even deploy sensor nodes to uninterested grids or dead areas. As a result, the probability-based approaches do not require more extra sensor nodes when the number of extra added NIS increases.
The number of sensor nodes of all evaluated approaches under various obstacle sizes is shown in Fig. 13, in which the obstacle size is selected from 1, random of (1, 4) and random of (1, 4, 9). As Fig. 13 shown, the number of sensor nodes of Random increases significantly as the obstacle size increases. Additionally, those of MAX_AVG_COV and MAX_MIN_COV increase gently as the obstacle size increases. ESP does not affected by the obstacle size obviously. The number of sensor nodes of ESP under different sensing radii is shown in Fig. 14, where NDS = 400, NBS = 20 and Rt = 2. The numbers of sensor nodes of different NIS decrease as the sensing radius increasing. Since a sensor node with a larger sensing radius could cover more number of interested grids, ESP requires less number of placed sensors. The number of sensor nodes of ESP under different transmission radii is indicated in Fig. 15, where NDS = 400, NBS = 20 and Rs = 2. The numbers of sensor nodes of different NIS decrease as the transmission radius 120
number of sensors
180
ESP MAX_AVG_COV MAX_MIN_COV Random
NIS=20 NIS=40 NIS=60 NIS=80 NIS=100
100
number of sensors
210
150
120
80
60
40
90 20
60 0
30
2
1
random of (1,4)
random of (1,4,9)
Obstacle size
Fig. 13. The number of sensor nodes under various obstacle sizes.
3
4
5
6
Transmission radius (Rt)
Fig. 15. The number of sensor nodes of ESP under different transmission radii (Rs = 2).
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ESP MAX_AVG_COV
Sensed But Fail to Transmit (SBFT)
0.5
MAX_MIN_COV Random
0.4
0.3
0.2
0.1
0
100
400
900 NDS
1600
2500
Fig. 16. Sensed but fail to transmit (SBFT) of different approaches under various NDS (Rs = 2, Rt = 1, NBS = 20).
increasing, because a longer transmission range requires less number of intermediate sensor nodes. For evaluating the coverage of placed sensor nodes, we compare the rate of sensed but fail to transmit (i.e., SBFT) of all approaches under various NDS, as indicated in Fig. 16. Lower SBFT means better coverage. In Fig. 16, ESP yields 0 SBFT, i.e., ESP achieves 100% coverage for all interested grids. The Random approach yields competitive SBFT to ESP. MAX_MIN_COV yields the highest
SBFT, which has the worst coverage. The SBFT of MAX_ AVG_COV is higher than that of Random. Additionally, the SBFTs of MAX_MIN_COV and MAX_AVG_COV increase as NDS increasing. The reason of the coverage results in Fig. 16 is that ESP considers the sensing and transmission radii simultaneously while placing sensor nodes, but other approaches do not. Finally, the placed results of all approaches with NDS = 400, NBS = 20 and NIS = 15 under random obstacle sizes of 1, 4 or 9 are demonstrated in Fig. 17a–d. ESP yields the least number of sensor nodes for covering all interested grids, but the others require more number of sensor nodes. In summary, ESP deploys the least number of sensor nodes as compared to other approaches under various situations, including different sizes of WSN, different number of interested grids, different number of obstacles grids, and different obstacle sizes. Additionally, ESP obviously reduces the number of sensor nodes while the sensing radius or the transmission radius increasing. 5. Conclusions and future works Not all the areas of a real environment are interested for sensing or monitoring as the network size increasing. The sensor node placement for covering entire filed causes several disadvantages, including costly, inefficient data forwarding and long transmission delay. Additionally, the
Fig. 17. The placement results of four approaches under the same environment, where the green circle is a border, the blue circles are sensor nodes, the pink squares are interested grids, and the black grids are obstacles. (a) The placement result of ESP. (b) The placement result of MMAX_MIN_COV. (c) The placement result of MAX_AVG_COV. (d) The placement result of Random. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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probability-based placement approaches are not suitable for such a WSN with sparse interested grids. An efficient sensor placement approach is thus proposed to deploy sensor nodes for covering interested grids in a sparse WSN. Obstacles are considered in ESP to meet real environments, where obstacles may block the data transmission between two sensor nodes. It thus requires some extra intermediate sensor nodes to relay data transmission. Moreover, different sensing and transmission radii are considered in ESP. Numerical results demonstrated that ESP outperforms other approaches in the number of placed sensor nodes under various situations, including different sizes of WSN, number of interested grids, number of obstacles and SBFT. ESP thus reduces sensor-deployment cost and data transmission delay. For increasing reliability, ESP adopts a dynamic re-deployment mechanism to place new sensors or to add some extra interested grids when some placed sensors fail. Finally, the worst case of time complexity of ESP, O(K2), is analyzed, which is better than that of the probability-based approaches, O(N2), where K is the number of interested grids and N is the number of grids. Many aspects of dynamic routing of sensed information in the wireless sensor networks require further study. For instance, we are currently investigating an efficient routing for distributed the query message in WSN with supporting local repair mechanism to increase the transmission reliability and the packet delivery rate. References [1] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey, Computer Networks 38 (4) (2002) 393– 422. [2] K.-I. Hwang, J. In, N. Park, D.-S. Eom, A design and implementation of wireless sensor gateway for efficient querying and managing through world wide web, IEEE Transactions on Consumer Electronics 49 (4) (2003) 1090–1097. [3] T.T. Hsieh, Using sensor networks for highway and traffic applications, IEEE Potentials 23 (2) (2004) 13–16. [4] D. Ganesan, A. Cerpa, W. Ye, Y. Yu, J. Zhao, D. Estrin, Networking issues in wireless sensor networks, Journal of Parallel and Distributed Computing 64 (7) (2004) 799–814. [5] M. Cardei, J. Wu, Coverage in wireless sensor networks, Handbook of Sensor Networks, CRC Press, 2004. [6] J. Carle, D. Simplot, Energy efficient area monitoring by sensor networks, IEEE Computer 37 (2) (2004) 40–46. [7] J. Zhang, H. Shi, Energy-efficient routing for 2D grid wireless sensor networks, in: Proceedings ITRE2003, August 2003, pp. 311–315. [8] J.-H. Chang, L. Tassiulas, Maximum lifetime routing in wireless sensor networks, IEEE/ACM Transactions on Networking 12 (4) (2004) 609–619. [9] S. Lindsey, C. Raghavendra, K.M. Sivalingam, Data gathering algorithms in sensor networks using energy metrics, IEEE Transactions on Parallel and Distributed Systems 13 (9) (2002) 924–935. [10] W. Yuan, S.V. Krishnamurthy, S.K. Tripathi, Synchronization of multiple levels of data fusion in wireless sensor networks, in: IEEE Globecom’03, vol. 1, December 2003, pp. 221–225. [11] D. Stark, J. Davis, Friendly Object Tracking and Foreign Object Detection and Localization with an SDAC Wireless Sensor Network, in: FTDCS 2004, May 2004, pp. 30–36.
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Ben-Jye Chang received his M.S. degree in computer engineering from University of Massachusetts, Lowell, in 1991 and the Ph.D. degree in computer science and information engineering from National Chung-Cheng University, Taiwan, in 2001. He joined the Department of Computer Science and Information Engineering faculty at Chaoyang University of Technology, Taiwan, in 2002, where he is currently an Associate Professor. His research interests include seamless roaming/vertical handoff in heterogeneous wireless networks, mobile computing in wireless sensor networks, QoS-based wireless networks, resource management for mobile communications, and TCP congestion control in heterogeneous wireless networks.
Jia-Bin Peng received his M.S. degree in networking and communication engineering from Chaoyang University of Technology, Taichung, Taiwan, in 2005. His current research interests include sensor deployment in wireless sensor networks, wireless networking and mobile computing, and all IP-based mobile communications.